In recent years, there has been an increasing number of cases in which drive recorders that are mounted on vehicles in order to perform forensics recording and so on are utilized and recorded video and vehicle-behavior data are utilized to perform driving-hazard analyses and situation visualization. In driving-hazard analyses, in order to identify dangerous scenes and analyze situations, it is desirable to perform a detailed analysis of dangerous vehicle behavior, such as abrupt deceleration, by using high-accuracy vehicle-speed information.
As a recent trend, simplified drive recorders that record vehicle speeds obtained from the global positioning system (GPS), instead of high-accuracy vehicle speeds based on vehicle-speed pulses, are increasing in number because of their ease of installation that does not involve work for connecting wires to vehicles. Vehicle speeds obtained from the GPS are sufficiently accurate for the purpose of recording, but are not sufficiently accurate for the purpose of analyses. The lack in accuracy has been a bottleneck when vehicle speeds are used for analyses. Accordingly, it is desired to estimate vehicle speeds by using a method other than the methods using vehicle-speed pulses and the GPS.
One available method for estimating a movement distance and a movement speed of a vehicle based on video acquired by a vehicle-mounted camera is a method for performing estimation based on feature-point tracking. In this estimation method, a monocular camera mounted on a moving vehicle captures video to time-sequentially track groups of feature points detected from images at individual time points. A translation and a rotation that indicate a geometric positional relationship between the images which satisfies a correspondence relationship of the groups of feature points between the images are estimated as motion parameters, and a movement distance and a movement speed are estimated based on the magnitude of the translation.
For example, there is a distance measuring device that measures a distance to an object that is located outside a moving body traveling on a road surface (see, for example, Japanese Laid-open Patent Publication No. 2006-349607).
The distance measuring device tentatively determines spatial coordinates of a fixed point in a three-dimensional space by using an arbitrary scale, based on feature points in images captured at different time points by monocular image capturing means. Based on the spatial coordinates of the fixed point located on the road surface, the distance measuring device determine a plane equation for an approximate plane representing the road surface that is being traveled, and determines a height from an approximate plane of the viewpoint of the camera in the coordinate system of the spatial coordinates, based on the determined plane equation. Based on the height from the approximate plane from the viewpoint of camera and the mounting height of the image capturing means from the road surface, the distance measuring device determines a scale of the spatial coordinates and modifies the spatial coordinates of the fixed point based on the determined scale.
There is also a three-dimensional-information restoration device that determines a correction coefficient based on distance information of a photographic target, the distance information resulting from measurement performed by a distance sensor, and thereby converts distance information determined from images of the photographic target into an absolute quantity (see, for example, Japanese Laid-open Patent Publication No. 2006-337075).
There is also a separator-line recognition device that recognizes divider lines, such as white line, drawn on a road (for example, Japanese Laid-open Patent Publication No. 2002-236912).
There is also a traveling-road detecting device that detects lane markers at both sides of a driving lane from an image of a traveling road behind a vehicle and that determines traveling-road-model parameters including horizontal displacement of the vehicle based on position information regarding the lane markers (see, for example, Japanese Laid-open Patent Publication No. 2002-352226).
A three-dimensional environment generation method using a feature point flow model is also available (see, for example, Kawanishi Ryosuke, Yamasita Atsushi, and Kaneko Toru, “Construction of 3D Environment Model by Using Omni-Directional Camera—Estimation of Camera Motion with a Feature Flow Model-”, MIRU2009, July, 2009). In the three-dimensional environment generation method, through utilization of the fact that the same feature points are measured between adjacent observation points when measurement results of different measurement points are combined, the scales are made to match each other so as to minimize the sum of squares of an error in the three-dimensional coordinates.
Bundle adjustment for numerically executing, in a problem for estimating parameters of a geometric model from images, nonlinear optimization with the aim of achieving high estimation accuracy has been available (see, for example, Okatani Takayuki, “Bundle Adjustment”, IPSJ SIG Technical Report, 2009).